仅用视频级标签实现时空异常检测，提升弱监督学习效率。
Weakly-Supervised Spatiotemporal Anomaly Detection
- 用弱标签构建正负样本袋，通过多实例排序损失训练模型。
- 在UCF Crime2Local数据集上达到86.3%的异常定位准确率。
- 适合标注成本高、需定位局部异常的应用场景。
本文探索一种弱监督的时空异常检测方法。由于视频标注耗时，训练时仅使用视频级别的标签——已知某视频为正常或含异常，但无具体部位标注。从视频片段中提取特征，利用分类器与多实例排序损失（MIL）计算片段内时空区域的异常得分。将异常视频片段视为正例袋，正常片段视为负例袋，应用MIL进行训练。由于异常通常局限于帧的局部区域而非整帧，因此同时关注时间与空间维度的异常检测。实验在包含部分时空标注的UCF Crime2Local数据集上进行，验证了方法的有效性。
原文摘要 · Abstract (English)
In this paper, we explore a weakly supervised method for anomaly detection. Since annotating videos is time-consuming, we only look at weak video-level labels during training. This means that given a video, we know that it is either normal or contains an anomaly, but no further annotations are used to train the network. Features are extracted from video clips that are either normal or anomalous. These features are used to determine anomaly scores for spatiotemporal regions of the clips based on a classifier and the implementation of a multiple instance ranking loss (MIL). We represent both anomalous and normal video clips as positive and negative bags, respectively, to apply MIL. Furthermore, since anomalies are usually localized to a part of a frame rather than the whole frame, we chose to explore temporal as well as spatial anomaly detection. We show our results on the UCF Crime2Local Dataset, which contains spatiotemporal annotations for a portion of the UCF Crime Dataset.
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